Gene expression studies for the analysis of domoic acid production in the marine diatomPseudo

Gene expression studies for the analysis ofdomoic acid production in the marine diatomPseudo-nitzschia multiseriesBoissonneault et al.

Boissonneault et al. BMC Molecular Biology 2013, 14:25http://www.biomedcentral.com/1471-2199/14/25

Boissonneault et al. BMC Molecular Biology 2013, 14:25http://www.biomedcentral.com/1471-2199/14/25

RESEARCH ARTICLE Open Access

Gene expression studies for the analysis ofdomoic acid production in the marine diatomPseudo-nitzschia multiseriesKatie Rose Boissonneault1,2*, Brooks M Henningsen1,3*, Stephen S Bates4, Deborah L Robertson5*, Sean Milton2,6,Jerry Pelletier7, Deborah A Hogan8 and David E Housman2

Abstract

Background: Pseudo-nitzschia multiseries Hasle (Hasle) (Ps-n) is distinctive among the ecologically important marinediatoms because it produces the neurotoxin domoic acid. Although the biology of Ps-n has been investigatedintensely, the characterization of the genes and biochemical pathways leading to domoic acid biosynthesis hasbeen limited. To identify transcripts whose levels correlate with domoic acid production, we analyzed Ps-n underconditions of high and low domoic acid production by cDNA microarray technology and reverse-transcriptionquantitative PCR (RT-qPCR) methods. Our goals included identifying and validating robust reference genes for Ps-nRNA expression analysis under these conditions.

Results: Through microarray analysis of exponential- and stationary-phase cultures with low and high domoic acidproduction, respectively, we identified candidate reference genes whose transcripts did not vary across conditions.We tested eleven potential reference genes for stability using RT-qPCR and GeNorm analyses. Our results indicatedthat transcripts encoding JmjC, dynein, and histone H3 proteins were the most suitable for normalization of expressiondata under conditions of silicon-limitation, in late-exponential through stationary phase. The microarray studiesidentified a number of genes that were up- and down-regulated under toxin-producing conditions. RT-qPCRanalysis, using the validated controls, confirmed the up-regulation of transcripts predicted to encode a cycloisomerase,an SLC6 transporter, phosphoenolpyruvate carboxykinase, glutamate dehydrogenase, a small heat shock protein, andan aldo-keto reductase, as well as the down-regulation of a transcript encoding a fucoxanthin-chlorophyll a-c bindingprotein, under these conditions.

Conclusion: Our results provide a strong basis for further studies of RNA expression levels in Ps-n, which will contributeto our understanding of genes involved in the production and release of domoic acid, an important neurotoxin thataffects human health as well as ecosystem function.

Keywords: Gene expression, Gene regulation, cDNA microarray, RT-qPCR, Normalization, Reference gene, Domoic acid,Pseudo-nitzschia multiseries, Bacillariophyceae, Diatom

* Correspondence: [email protected]; [email protected]; [email protected] of Biological Sciences, Plymouth State University, MSC 64, 17 High St.,Plymouth, NH 03264, USA5Biology Department, Clark University, 950 Main Street, Worcester, MA 01610, USAFull list of author information is available at the end of the article

© 2013 Boissonneault et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of theCreative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.

mailto:[email protected]




http://creativecommons.org/licenses/by/2.0

Boissonneault et al. BMC Molecular Biology 2013, 14:25 Page 2 of 18http://www.biomedcentral.com/1471-2199/14/25

BackgroundThe marine diatom Pseudo-nitzschia multiseries Hasle(Hasle) (Ps-n) produces the neurotoxin domoic acid(DA), which causes amnesic shellfish poisoning (ASP)[1-4]. DA is a neuroexcitatory, water-soluble amino acidthat exhibits structural similarity with the neurotransmitterglutamate [5]. DA binds with high affinity to glutamatereceptors, leading to excitation and ultimately cell death ofneurons exposed to this toxin [6]. Production of DAby Ps-n, and at least 14 other members of the genusPseudo-nitzschia, has been verified in oceanic regionsthroughout the world, primarily in coastal and upwellingzones [7,8]. The documented effects of DA on humans,birds, finfish, cephalopods, and marine mammals, andthe economic costs of shellfishery closures due to DAcontamination, has generated ongoing interest in un-derstanding the regulation and control of DA productionin this genus [7,9-11]. Yet, the biosynthetic pathwaysleading to DA production and the genes that governthese pathways remain unresolved [12,13].Numerous studies on Ps-n growth dynamics have shown

that DA production does not begin until early stationaryphase, i.e. toxin is not typically produced in detectableamounts during the exponential growth phase (reviewedin [9]). In other studies that exposed Ps-n to conditionsthat slowed cell division during the mid-exponentialphase, cells produced low levels of toxin. Therefore, toxinproduction appears to be associated with stages in the cellcycle when cell division has slowed or stopped due tosome limiting nutrient factor, most notably silicon (Si)[10,14]. In addition, several bacterial isolates have beenshown to enhance DA production by Ps-n [15-17]. Ps-ncan produce DA in axenic cultures [2,18], yet, reintroduc-tion of bacteria to axenic cultures results in increased Ps-nDA production [15-17].In this study, we developed a Ps-n cDNA library

and used it to construct a microarray in order toscreen for genes that were differentially expressedunder high-toxin-producing versus low-toxin-producingconditions. A total of 5,265 Ps-n cDNAs were printedin replicate, and mRNAs from cells that were in late-exponential growth phase were compared to thosethat were in stationary phase in both axenic and non-axenic cultures. Using these array data, we identifiedcandidate reference and target genes for further study.Eleven reference genes were evaluated for stability inreverse-transcription quantitative PCR (RT-qPCR) analysesof Ps-n mRNA from Si-limited cultures. We performed aGeNorm analysis to validate transcripts that did notvary across conditions. Using the validated referencetranscripts, we then confirmed the differential regulationof several transcripts whose expression correlates with DAproduction. These findings will facilitate future workaimed at elucidating the DA biosynthesis pathway and

identifying transcriptional biomarkers indicative of DAproduction.

ResultsPseudo-nitzschia growth and toxin production formicroarray studiesSamples for microarray analysis were obtained fromthree biological experiments using Ps-n strain CL-125.These trials included one axenic and two non-axeniccultures, all grown in standard medium f/2. DA productionbegan at the onset of stationary phase and continued toincrease over time in all three experiments (Figure 1). FinalDA concentrations, expressed on a per mL basis, were ~30times lower in the axenic growth experiment compared tothe non-axenic growth experiments, as expected based onprevious studies [2,15-18]. Previous studies also indicatedthat Si is the limiting nutrient for Ps-n cells grown in batchcultures with medium f/2 [9,10,14]; therefore, we presumethat the cells in these experiments were Si-limited duringstationary phase. Samples were harvested for microarrayanalysis during the late-exponential and stationary phasesto compare gene expression between low-toxin-producingvs. high-toxin-producing cells. These time points areindicated by arrows in Figure 1a, 1b.

Identification and validation of reference transcriptsOur initial goal was the identification of transcripts whoseexpression levels were stable between late-exponentialand stationary phases, which could then be used fornormalization of other transcripts’ expression levelsunder these conditions. We selected eleven candidatereference genes to evaluate in RT-qPCR studies basedon their stability in the microarray results as well astheir biological roles and use as controls in previousstudies (Table 1; Additional file 1). These included tran-scripts encoding: dynein, histone H3, cyclophilin, ubiquitin,elongation factor 1 alpha (EF-1α), phosphoglyceratekinase 1 (PGK), eukaryotic initiation factor 2 (eIF-2),a JmjC-domain containing protein (JmjC), an AAA-domaincontaining ATPase, glyceraldehyde-3-phosphate dehydro-genase (GAPDH), and 18s rRNA. RT-qPCR primer sets foreach candidate reference gene were designed and tested,and exhibited high sequence specificity and PCR efficiencyunder our assay conditions with an annealing temperatureof 60°C (Table 2).To validate the stability of candidate reference genes, bio-

logical triplicates of Ps-n strain GGB1 were grown undernon-axenic, Si-limited conditions. RNA was harvested atmultiple time points during the late-exponential and sta-tionary phases (Figure 1c). The initiation of DA productionagain corresponded with the onset of stationary phase,which in this study was on day four. Initial silicate concen-trations were reduced to 37.2 μM in the culture medium(vs. 107 μM in the standard f/2 medium). The measured

a b

c d

μμ

Figure 1 Change in cell number and DA production as a consequence of growth under non-axenic and axenic conditions. a) Ps-n strainCL-125, Non-axenic culture experiments 1 (solid) and 2 (open). b) Ps-n strain CL-125, Axenic culture experiment. Cells were harvested forRNA extraction on the days indicated by arrows. c) Increase in cell number (squares) and in DA concentration (circles) of Ps-n strainGGB1 in non-axenic, triplicate cultures. Cells were harvested for RNA extraction on days 3–10. d) Nutrient concentrations over time inGGB1 cultures (nitrite/nitrate, phosphate, silicate). Data for (c) and (d) represent the mean change of triplicate samples (± 1 SD).


silicate concentration was below 1.0 μM by day four(Figure 1d), corresponding with the entry into stationaryphase. Nitrate and phosphate appear to be present insufficient quantities throughout the experiment (Figure 1d)[9,10,14], further supporting that growth of these cultureswas Si-limited.In an initial GeNorm analysis, all eleven candidate

genes were tested for stability across a subset of samplesfrom the Si-limitation experiment, including replicatesfrom days 3, 4, and 10 (Figure 2a). The four genes withthe best stability values (M-value), i.e. JmjC (0.33),dynein (0.33), histone H3 (0.37) and cyclophilin (0.39),along with EF-1α (0.62), were further evaluated forstability using GeNorm analysis across the complete setof expression data for the Si-limitation experiment(Figure 2b). All five genes showed acceptable stability(M-value <0.5) when evaluated across the complete setof data. GeNorm pairwise-variation analysis determinedthat only two genes (JmjC, dynein) were necessary for sub-sequent normalization. However, since the JmjC, dynein,

and histone H3 genes had equivalent M-values and werematched for 1st rank, we used all three for normalizationof the expression data as described below. The expressionprofiles of the reference genes show the stability of thesetop-ranked genes, and the slight variability of the EF-1αand cyclophilin genes (Figure 3).

Identification and verification of differentially expressedtranscriptsIn the microarray study, only those transcripts that wereup- or down-regulated in all three trials were consideredfurther (Tables 3 and 4; Additional file 1). Highertranscript levels in stationary (high-toxin-producing)as compared to late-exponential (low-toxin-producing)phase were observed for 12 transcripts, correspondingto 76 cDNA clones printed on the Ps-n array (Table 3;Additional file 1); reduced transcript levels underthese conditions were observed for six genes, corre-sponding to 17 cDNA clones printed on the array(Table 4; Additional file 1). In addition to those genes

Table 1 Expression data for candidate reference genes from Pseudo-nitzschia multiseries (Ps-n) cDNA microarray analysisa

Fold changeb

Stationary versus late-exponential phase

Ps-n NRIdentifier

JGI Ps-n Genome hit Non-axenic Non-axenic Axenic Predicted gene product

Scaffold:Start-End Expt. 1 Expt. 2 Expt.

53B6 41:203449-205143 1.05 ± 0.01 1.04 ± 0.05 1.05 ± 0.03 JmjC-domain family protein (JmjC)

45E3 55:316954-329784 1.10 ± 0.02 1.24 ± 0.07 1.36 ± 0.05 Dynein heavy chain, cytosolic

177F1 198:180888-181958 1.05 ± 0.02 0.93 ± 0.01 0.81 ± 0.01 Histone H3

PSN0918 2485:4610-6293 1.30 ± 0.00 1.29 ± 0.37 0.99 ± 0.01 Cyclophilin

PSN0001 10:398258-400584 1.37 ± 0.10 1.19 ± 0.07 1.10 ± 0.17 Elongation factor 1-alpha (EF-1α)

PSN0547 210:148023-149837 0.90 ± 0.08 0.78 ± 0.05 1.26 ± 0.03 Phosphoglycerate kinase (PGK)

PSN1327 890:26709-27681 1.00 ± 0.03 1.07 ± 0.00 1.24 ± 0.03 Elongation initiation factor 2 (eIF-2)

PSN0332 2:525315-527491 1.13 ± 0.05 1.29 ± 0.03 1.19 ± 0.17 ATPase with AAA domain

PSN0032 18:343323-346000 0.74 ± 0.04 0.68 ± 0.06 0.44 ± 0.03c Ubiquitin

PSN1138 68:114178-115516 0.87 ± 0.08 0.92 ± 0.09 1.73 ± 0.23c Glyceraldehyde-3-phosphate dehydrogenase (GAPDH)aPotential reference genes for RT-qPCR studies were selected based on their stability in the microarray results or their use as controls in previous studies.bThe fold-change data presented in Table 1 represents the average differences across all of the cDNA clones that were printed on the array for each transcript.As follows, the number of independent cDNA clones for each transcript was: 53B6 (1), 45E3 (1), 177F1 (1), PSN0918 (1), PSN0001 (58), PSN0547 (2), PSN1327 (1),PSN0032 (7), PSN0332 (2), PSN1138 (6). Each clone was printed in duplicate on array. 18s not printed on array.cTranscript levels showed statistically significant differences between samples.


identified based on the overall false discovery rate(FDR), we also included genes predicted to encodeglutamate dehydrogenase and fucoxanthin-chlorophylla-c binding protein (FCP) in our results, as both wereof interest in this study, and the local false discoveryrates (LFDR) indicated valid changes in gene expression.Fold-change differences were consistently lower in theaxenic growth experiment; however, the patterns ofexpression were comparable across the three growthexperiments.Eight genes were selected for further study using

RT-qPCR (Table 5). Of these, six genes had highertranscript levels during the stationary phase (Figure 4a),and were predicted to encode a cycloisomerase, an SLC6transporter, an aldo-keto reductase, glutamate dehydrogen-ase, phosphoenolpyruvate carboxykinase (PEPCK), and asmall heat shock protein. The cycloisomerase, SLC6 trans-porter, and aldo-keto reductase genes all had a statisticallysignificant step-wise increase in transcript abundance fromdays 3 to 5 that correlated with the gradual increase in DAproduction (Figures 1c, and 4a). The other up-regulatedgenes showed similar trends during this time period. FCPshowed decreased transcript levels during the transitionfrom exponential to stationary phase; the phosphofructoki-nase (PFK) transcript levels, which were down-regulated inthe microarray experiment, were not statistically differentas measured by RT-qPCR (Figure 4b). Of note, the absenceof DNA contamination in these studies is shown visu-ally by the parallel results from amplification ofcDNA using both standard and exon-exon spanningprimer sets for the cyclophilin, SLC6 transporter, andPFK genes (Figures 3 and 4). Potential connections

between these transcripts and DA production arediscussed below.

DiscussionOur data support the validity of the reference genes,JmjC, dynein, and histone H3, as suitable controls fornormalization of Ps-n mRNAs under conditions ofSi-limitation, as cells transition from late-exponentialto stationary phase (i.e. from minimal to high DAproduction). Multiple reference genes are typicallymore effective for accurate normalization [19,20];therefore, we recommend the use of the geometric meanof these three reference genes for normalization of Ps-nRT-qPCR expression data under these conditions. Thestability of a histone H3 gene, whose expression oftenvaries with growth phase in other species, may be attributedto regulation at the level of translation vs. transcription[21]. Alternatively, the presence of four histone H3homologs in the Ps-n genome [22], as revealed byBLAST analysis, presents the possibility that we haveidentified a replication-independent family member, asshown in other studies [23-27]. Of note, our microarraydata indicate that the standard “housekeeping” genes,GAPDH and actin, may not be suitable reference genes inthis experimental system as their transcript levels varied(Table 1, Additional file 1). We tested GAPDH for stabilityin the RT-qPCR GeNorm analysis, and it showed theleast stable M-value of the candidate reference genestested. Similar results were observed in the diatomPhaeodactylum tricornutum [28]. These results highlightthe importance of validating reference genes prior to usefor normalization. The primer sets provided in Table 2

Table 2 RT-qPCR reference gene primer sequences and characteristics for all candidate reference genes

Predicted geneproduct

Primer sequence GC (%) Tm (°C)a Amplicon (bp) Ex-Ex Spanningb Efficiency (%) R2

JmjC F: CCAGTTATGATTTCGGCAATAATGG 40.0 54.5 139 No 96.8 0.991

R: GGTGTCAGTTCATCGTCTTCAG 50.0 55.4

Dynein F: CGAAGCCAGTAGTGGTATCAAGG 52.2 57.1 84 No 98.0 0.991

R: CGAATCAGGTTGTTCTGGAGTCG 52.2 57.6

Histone H3 F: GAAGCCTACCTGGTGGGTCTC 61.9 59.3 151 No 101.4 0.999

R: CGTCCGATCACCTTCCGTCTC 61.9 59.5

Cyclophilin F: GTAGGACAAAGCCAGCACAACAGG 54.2 60.2 83 No 99.0 0.997

R: GAATGAATCGGTGCTCGTAGGAGG 54.2 59.2

Cyclophilin Ex-Ex F: CTGGGTTTCAAGAGCCAACGAC 54.5 58.5 105 Yes 98.3 0.997

R: CATCAATGCCGACGGACTGAAT 50.0 57.7

EF-1á F: GGACTCTCCATCAAGGGTATTGC 52.2 57.3 150 No 98.4 1.000

R: GTATCCAGGCTTGAGGACACC 57.1 57.4

PGK F: GATGCCGAGAAGAAGGGTGTG 57.1 58.0 69 No 98.7 0.996

R: CGAAGGAAATGCTTGTGTTGCGAC 50.0 59.2

eIF-2 F: GTGATGCGTGCTTGATTGCTTG 50.0 57.6 78 No 99.6 0.997

R: CCTTCATGTCGTGGCGAAGC 60.0 59.0

ATPasec F: GGTGGTGATATTGCTCCCTTG 52.4 55.7 164 No 98.4 0.996

R: CGTTGATCTTCACTGATCTTTAGTCG 42.3 55.4

Ubiquitin F: CCTTCGTCGGAACATCACTACC 54.5 57.3 126 No 94.1 0.997

R: CGTCAAGGGTGATAGTCTTGC 52.4 55.5

GAPDH F: GACAACTTCCACAAGGTCATCTCC 50.0 57.5 83 No 100.3 0.999

R: CTGGTGTAGACAGCCAAGTCG 57.1 57.6

18s rRNA F: GTTGCCCGCCACTCTTTACGATTG 54.2 60.6 81 No 98.0 0.998

R: GTATCAGTGCCAAGCCTCTGC 57.1 58.3

β-tubulin Ex-Exd F: CCAAATTCTGGCAGGTCATG 50.0 54.2 114 Yes 100.8 0.998

R: CTTGTCCCTCGTTGAAGTACAC 50.0 55.4aThe annealing temperature for all standard curve analyses was performed at 60°C to demonstrate the efficiency of the primer sets under our assay conditions;the calculated Tm values are provided for reference.b‘Ex-Ex spanning’ refers to primers that span an exon-exon junction; these primer sets did not yield a product using gDNA as a template.cThe PSN0332 contig sequence had an extra “t” in the reverse primer region as compared to the Pseudo-nitzschia multiseries genome sequence, yet the primer setdemonstrated high efficiency.dβ-tubulin was not used in the studies presented as it was not printed on the microarray. It is a validated exon-spanning primer set that demonstrated goodefficiency, and may have value as a reference gene for future studies.


should prove useful for examining changes in Ps-n geneexpression as populations transition from late-exponentialto stationary phase, when DA production is stimulated,and for testing these candidate genes for use as controlsunder other growth conditions.The overall correlation between the array and RT-qPCR

data in these studies offers proof of concept for thereliability of these expression data. Also, the differentialexpression of these genes was evidenced in two differentPs-n strains, CL-125 and GGB1, which were isolated fromAtlantic and Pacific coastal regions, respectively. Thedown-regulation of an FCP gene during stationary phase,when photosynthesis and chlorophyll presumably declinee.g. [29], also supports the validity of these results. Thedown-regulation of FCP gene expression has been

correlated with stationary phase and decreases inlight-harvesting pigments in the related pennate diatomPhaeodactylum tricornutum [30], as well as other marinealgae [31,32]. The pathways leading to chlorophyll andDA production are both predicted to draw on a pool ofglutamate [12,33], so the down-regulation of FCP in Ps-ncorrelates well with the onset of DA production.Our results provide a framework to further investi-

gate the control of toxin production in Ps-n. Previous 13C-and 14C-labeling studies suggested a model involvingcondensation of an activated glutamate intermediate de-rived from the citric acid cycle with an isoprenoid inter-mediate, and subsequent cyclization as a mechanism togenerate DA [12]. These studies allow us to generatehypotheses regarding the biological function of the

a

b

Figure 2 Average expression stability (M-value) of the referencegenes determined by GeNorm analysis. An individual referencegene is tested against the other reference genes in a pairwise variationthat serially excludes the least stable genes from the analysis. The moststable reference genes exhibit the lowest M-values. The accepted cut-off for stability of reference genes is an M-value of 0.50. a) In the initialanalysis, which tested a subset of mRNA samples under Si-limited con-ditions for all eleven of our potential reference genes, four referencegenes were determined to be acceptably stable. b) In the analysis ofthe Si-limited growth experiment, these four reference genes and EF1-alpha were tested. All five showed acceptable stability across themRNAs, and JmjC, Dynein, and Histone H3 were tied for the 1st rank.


genes identified in our study relative to DA produc-tion. For example, up-regulation of a gene encoding aputative Ps-n cycloisomerase is intriguing as its productmay be directly involved in the proposed cyclization stepleading to the pyrrolidine ring in DA. Alternatively, thecycloisomerase, similar to other enzymes in the relatedpfam20282 group, may be involved in converting aromaticcompounds into citric acid cycle intermediates, proposedto feed the pathway leading to DA synthesis [34-38]. Theidentification of a differentially expressed transcript en-coding a member of the SLC6 amino acid transporter

family is also interesting. The translated Ps-n openreading frame aligns most closely with characterizedγ-aminobutyric (GABA) neurotransmitter transporters[39], suggesting the hypothesis that the Ps-n trans-porter is involved in movement of DA, or a syntheticprecursor, into or out of cells.Our discovery of the up-regulation of a predicted

cycloisomerase belonging to the lactonase/lactonizingfamily, as well as the SLC6 transporter, entertains thespeculation that these gene products are involved incommunication between Ps-n cells, or Ps-n and bacteria.The parallels with GABA in plant signaling pathways[40-42] pose a potential role for DA in Ps-n biology,which has not yet been defined [7,9]. For example,GABA produced by wounded plant tissues appears to con-trol the lactone quorum-sensing signal in Agrobacteriumtumefaciens by regulating the A. tumefaciens lactonasegene [41]. Bacterial production of lactones in Ps-n culturesis correlated with increased DA production [16,17,43], sug-gesting a possible relationship between DA and quorumsensing [44]. Characterization of the predicted cycloisome-rase’s enzymatic properties will be of significant interest inrelation to these hypotheses. Similarly, demonstration thatthe SLC6 transporter is involved in movement of DA intoor out of the cell would be a valuable contribution to un-derstanding the role of DA in Ps-n biology. While we havetaken the perspective that DA may function in signalingpathways, including quorum sensing or pheromone com-munication [7,8,42], some studies suggest that DA mayfunction as a chelating agent [45-47]. Hence, studyingthe transport of DA into and out of Ps-n cells directlywould contribute to describing the role(s) of DA inPs-n biology. A family of four SLC6 transporters wasidentified in the recently released Ps-n draft genome[22,39], so characterization of this entire family shouldadvance our understanding of Ps-n biology.Many of the differentially expressed genes in this study

relate to general metabolic pathways. Therefore, furtherinvestigation is needed to resolve the role of these genesin relation to both the growth state and DA synthesis inPs-n. For example, the up-regulation of PEPCK, as wellas the potential down-regulation of PFK, suggests achange in energy metabolizing pathways as Ps-n cellstransition from exponential to stationary phase, consistentwith a shift to gluconeogenesis and carbon metabolismthrough the citric acid cycle [48,49]. Similarly, glutamatedehydrogenase, which catalyzes the reversible conversionof glutamate and the citric acid cycle intermediateα-ketoglutarate, is a key enzyme involved in nitrogenand energy metabolism [50,51]. In addition, the differen-tial expression of a predicted acyl-CoA synthetase (Table 3)suggests the possibility that lipids and fatty acids are beingbroken down, and while this may be a physiologicalresponse to growth-limiting conditions, the products

αα

Figure 3 RT-qPCR analysis of candidate reference genes for normalization from Pseudo-nitzschia multiseries. Bars represent the meanchange (± 1 SD) in expression relative to Day 3. Open bars represent measurements using primers that spanned an exon-exon junction; grey barsrepresent measurements using standard primers that did not span an exon-exon junction. Means were not significantly different (p < 0.05), exceptEF-1α. Means with different letters were significantly different (p < 0.05). Statistical analyses were performed using a general linear model ANOVAwith Bonferroni post-hoc test, 95% confidence intervals.


could then be channeled as precursors into DA synthesis.Previous studies have shown that Ps-n lipid contentdecreases in response to Si deficiency during stationaryphase [9,52]. Acyl-CoA synthetases are also involved inamino acid acylation, so could be directly involved inthe condensation of the glutamate and isoprenoid-likemoieties [13,53,54].A small heat shock protein gene was most highly

up-regulated later in the stationary phase as determinedby RT-qPCR, suggestive of its expression relative tophysiological stress. The aldo-keto reductase transcriptlevels showed a step-wise progression from the exponen-tial into the stationary phase, with the highest expressionlevels later in stationary phase, as well. The expressionpatterns of these genes may be useful for monitoring thephysiological state of Ps-n cells. The aldo-keto reductasemay also have a functional role in DA synthesis, as thelabeling studies indicated that the C7’ in DA is selectivelyoxidized to a carboxyl group [12]. Several of the genes thatwere identified as being up-regulated in this studyhave not been previously characterized from diatoms

and represent potential targets for further studies ofDA synthesis.The enhancement of DA production by co-existing

microbes is a complex and fascinating aspect of DA biology[7]. A limited number of genes in our study indicatedsignificantly different expression patterns between thenon-axenic vs. axenic growth experiments. For example,a subtilisin-like gene, predicted to encode a secreted pro-tease, was up-regulated in the non-axenic cultures relativeto the axenic culture (Additional file 2). In addition, mi-crobes may influence the metabolic pathways predicted tobe involved in fatty acid production. Ramsey et al. [12]suggested that the principal pathway to the isoprenoidportion of DA is via an alternative glyceraldehyde 3-phosphate (G3P)-independent route, and it is interestingto note that glyceraldehyde-3-phosphate dehydrogenase(GAPDH) was up-regulated only in the axenic growth ex-periment (Table 3), suggesting that bacteria may influencethis pathway. Future studies will focus on the specific rolesthat co-existing microbes play in the regulation of Ps-ngenes and domoic acid production.

Table 3 Transcripts at higher levels in stationary (high-toxin-producing) as compared to late-exponential (low-toxin-producing) phase in Pseudo-nitzschia multiseries (Ps-n) as determined by cDNA microarray analysis

Fold changea


Ps-n NRIdentifier

JGI Ps-n Genome hit Non-axenic Non-axenic Axenic Predicted gene product


PSN0011 481:47438-49830 3.99 ± 0.96 3.46 ± 1.90 2.19 ± 0.37 Cycloisomerase (pfam10282 lactonase/lactonizing enzyme),

COG2706 (3-carboxymuconate cyclase)

PSN0072 269:13929-16593 3.40 ± 0.38 3.39 ± 0.32 2.01 ± 0.24 SLC6, Sodium and Chloride-dependent amino acid transporter

PSN0014 2396:2127-4845 4.64 ± 0.89 4.10 ± 0.89 2.14 ± 0.27 Acyl-CoA synthetase with transit peptide

PSN0016 37:106113-109070 3.78 ± 0.27 3.01 ± 0.29 3.11 ± 0.50 Phosphoenolpyruvate carboxykinase, ATP-

dependent with transit peptide (PEPCK)

PSN0025 155:75271-76268 6.65 ± 1.56 7.07 ± 1.75 4.18 ± 0.57 Small heat shock protein with alpha-crystallin

domain, chloroplastic (sHSP)

PSN0052 70:256654-258177 3.89 ± 0.59 2.61 ± 0.21 1.57 ± 0.01 Mitochondrial carrier protein

PSN0015 66:296511-298386 3.19 ± 0.57 3.15 ± 0.43 1.86 ± 0.17 Aldo-keto reductase with signal peptide

PSN0042 21:457033-458813 5.47 ± 0.75 7.52 ± 1.69 3.37 ± 0.50 Predicted protein with signal peptide

6H1 117:15135-16102 5.45 ± 0.55 3.76 ± 0.00 2.57 ± 0.04 Predicted protein with signal or transit peptide

73D12 1312:10048-11278 3.45 ± 0.11 4.01 ± 0.03 1.63 ± 0.07 Ps-n specific, no hits in NR or Swissprot

46A5 447:56474-57752 4.36 ± 0.00 4.61 ± 0.11 1.73 ± 0.02 Ps-n specific, no hits in NR or Swissprot

17F11 303:41858-44529 5.42 ± 0.18 6.02 ± 0.85 2.07 ± 0.06 Predicted protein with glycosyltransferase domain

PSN1428b 95:293619-297233 2.22 ± 0.26 1.86 ± 0.20 1.81 ± 0.37 NAD-specific glutamate dehydrogenase (GDH)aThe fold-change data presented are the average of all of the cDNA clones that were printed on the array for each transcript. The number of cDNA clones for eachtranscript was: PSN0011 (21), PSN0072 (3), PSN0014 (14), PSN0016 (14), PSN0025 (5), PSN0052 (4), PSN0015 (7), PSN0042 (4), 6H1 (1), 73D12 (1), 46A5 (1), 17F11 (1),PSN1428 (2). Each clone was printed on the array twice.bPSN1428 was included as a gene of interest in RT-qPCR analysis, below, although it did not meet our statistical criteria for the original microarray analysis. Pleasesee Methods for statistical analysis, and Additional file 1 for FDR and LDFR data.

Table 4 Transcripts at lower levels in stationary (high-toxin-producing) as compared to late-exponential (low-toxin-producing) phase in Pseudo-nitzschia multiseries (Ps-n) as determined by cDNA microarray analysis

Fold changea


Ps-n NRIdentifier

JGI Ps-n Genome Hit Non-axenic Non-axenic Axenic Predicted gene product


PSN0100 32:397085-398987 0.34 ± 0.01 0.33 ± 0.05 0.39 ± 0.01 Pyrophosphate-dependent phosphofructokinase (PFK)

PSN0060 133:16659-18505 0.20± 0.02 0.16 ± 0.03 0.44 ± 0.04 Predicted protein with signal or transit peptide

PSN0048 188:179178-180986 0.36± 0.08 0.25 ± 0.06 0.52 ± 0.05 Predicted protein with signal or transit peptide

PSN0080 461:123066-124152 0.32 ± 0.02 0.33 ± 0.03 0.57 ± 0.07 Predicted protein with mitochondrial transit peptide

135E4 1441:17433-18891 0.17 ± 0.00 0.19 ± 0.02 0.45 ± 0.02 Predicted protein

165G9 8:175639-176910 0.37 ± 0.02 0.37 ± 0.02 0.62 ± 0.04 Tetratricopeptide repeat protein

135H6b 214:78592-7971 0.41 ± 0.00 0.22 ± 0.02 0.59 ± 0.00 Fucoxanthin-chlorophyll a-c binding protein,

chloroplastic (FCP)aThe fold-change data presented are the average of all of the cDNA clones that were printed on the array for each transcript. The number of cDNA clones for eachtranscript was: PSN0100 (2), PSN0060 (5), PSN0048 (5), PSN0080 (3), 135E4 (1), 165G9 (1), 135H6 (1), and each clone was printed twice.b135H6 was included as a gene of interest in RT-qPCR analysis, below, although it did not meet our fold-change cut-off for the original FDR microarray analysis(yet, all LFDRs were <10%). Please see Methods for statistical analysis, and Additional file 1 for FDR and LDFR data.


Table 5 RT-qPCR target gene primer sequences and characteristics

Predicted geneproduct

Primer sequence GC(%)

Tm(°C)a

Amplicon(bp)

Ex-ExSpanning b

Efficiency(%)

R2

Cycloisomerase F: TCATAGGTGGCGTCAAGAACGTGT 50.0 60.3 127 No 99.5 0.995

R: TCAGCTTGTCGTGCCGAAATTGTG 50.0 60.3

SLC6 F: TCGGACACTACGGAGACTACG 57.1 57.1 73 No 104.2 0.997

R: ACCAAGGTGAAGGCGACG 61.1 58.0

SLC6 Ex-Ex F: CATGCACGATACTGTCTATTTCG 43.5 53.6 122 Yes 100.0 0.998

R: CGTCCAACCAAAATAAGCCAGC 50.0 57.0

Aldo-keto reductase F: GAATGGGCTACGGAGAGACG 60.0 57.3 114 No 99.5 0.998

R: GTACAGGCGTGAATTTGGTAGC 50.0 56.2

sHSP F: GACGAAGGATTCATCACCGTCG 54.5 57.7 141 No 102.9 0.998

R: GACACCGTTGTCGAGGGTAG 60.0 57.4

PEPCK F: GCATTGCTCTGCAAACGTCG 55.0 57.7 107 No 100.0 0.997

R: CAATCAAGGCTCGGTGAGGATC 54.5 57.7

GDH F: CAATGCCATCAACGCCATCAAGGA 50.0 60.2 128 No 98.4 0.998

R: CAAAGCCGAGGTTGGCAAGAGTTT 50.0 60.3

PFK F: CGAGGTGGCATCCAAACGATTGC 56.5 61.1 84 No 105.1 0.998

R: GCAGCCTGTGTATTGGTATCGTCG 54.1 59.7

PFK Ex-Ex F: GGAGAAAATCCGCTCGAGGTG 57.1 57.9 111 Yes 99.4 0.997

R: CTTTGAGAGAACCGCAGCCTG 57.1 58.4

FCP F: CGTCTCATACCACGGCAC 61.1 55.7 184 No 97.8 0.997

R: CTTGGATTGATGGTCCACGAG 52.3 55.6aThe annealing temperature for all standard curve analyses was performed at 60°C to demonstrate the efficiency of the primer sets under our assay conditions;the calculated Tm values are provided for reference.b‘Ex-Ex spanning’ refers to primers that span an exon-exon junction; these primer sets did not yield a product using gDNA as a template.


The Ps-n genome is predicted to include 19,703 genes[22]; thus, the estimated 3,675 non-redundant transcriptsmonitored via this microarray represent ~20% of the gen-ome. Future studies using RNA sequencing methods willdetermine if other transcripts related to those highlightedhere are also differentially expressed in correlation withDA production.

ConclusionsOur study identified a number of significantly up- anddown-regulated genes that provide the basis for futurestudies on DA production, growth state, stress, and aminoacid transport in Ps-n. The identified transcripts may beparticularly useful as early indicators of toxin productionand the switch of Ps-n cells to an alternative growth state.The reliability of RT-qPCR data will be enhanced by useof the validated internal reference genes presented in thisstudy. To our knowledge, this is the first identification andvalidation of reference genes for RT-qPCR studies in Ps-n.

MethodsPseudo-nitzschia multiseries strains and culture conditionsPs-n strain CL-125 was isolated by Claude Léger (Fisheriesand Oceans Canada, Gulf Fisheries Centre, Moncton,

New Brunswick, Canada) from a sample collected onSeptember 23, 2000, in Mill River (a brackish water es-tuary), Prince Edward Island, Canada. Cultures for themicroarray studies were grown in 0.2 μm-filtered, auto-claved seawater (from Woods Hole, MA) enriched withf/2 nutrients [55] and amended with 10-8 μM Se. Thesebatch cultures were grown in 15 L of f/2 medium in 19 Lborosilicate carboys, and incubated at 20°C. The irradi-ance was maintained at 100 μmol photons m-2 s-1, with a14:10-h light:dark (L:D) cycle for the cDNA library cul-tures, and continuous light for the experimental cultures.The cultures were aerated using aquarium pumps withsterile cotton and activated carbon filters and were con-stantly mixed with magnetic stirrers. An axenic culture ofCL-125 was obtained by antibiotic treatment for 72 h,using 1.6:0.8 mg mL-1 penicillin:streptomycin [2]. Thesewere tested for culturable bacteria by incubation inBacto-peptone broth (Difco Laboratories, Detroit, MI,USA; 1 g L-1 seawater) and 2216 Marine Agar (Difco)at ~20°C for at least 20 d.Ps-n strain GGB1 was isolated by Michael Carlson and

Kyle Frishkorn (University of Washington, Seattle, WA,USA) in July 2010, from Puget Sound, WA, USA. Cul-tures for the RT-qPCR study were grown in 0.45 μm-

Figure 4 RT-qPCR analysis of Pseudo-nitzschia multiseries genes whose expression was up-regulated (a) or down-regulated (b) in micro-array analysis. Bars represent the mean change (± 1 SD) in expression relative to Day 3. Open bars represent measurements using primers thatspanned an exon-exon junction; grey bars represent measurements using standard primers that did not span an exon-exon junction. Means withdifferent letters were significantly different (p < 0.05). Statistical analyses were performed using a general linear model ANOVA with Bonferronipost-hoc test, 95% confidence intervals.


filtered, autoclaved seawater (from Portsmouth Harbor,Newcastle, NH, USA) enriched with f/2 nutrients [55],except that the initial Si was lowered to 37.2 μM and fer-ric sequestrene was replaced with Na2EDTA • 2H2O andFeCl3 • 6H2O (Provasoli-Guillard National Center forMarine Algae and Microbiota). Before inoculation ofexperimental cultures, cells were maintained in exponen-tial growth for at least two preceding transfers. TriplicateGGB1 experimental cultures were grown in 2.6 L of f/2medium in 3-L polycarbonate baffled flasks, and incubatedat 15°C. The irradiance was maintained at ~100 μmol

photons m-2 s-1, with a 16:8-h L:D cycle. Flasks wereaerated by constant mixing supplied by magneticstirrers.

Sampling, toxin, and nutrient analysisThe microarray study included three biological repli-cates: two non-axenic cultures and one axenic culture.Samples were taken every two to three days for cellcounts and domoic acid (DA) analysis. Cell concentra-tions were estimated by averaging the number of cellsenumerated by light microscopy, using a Neubauer


hemacytometer chamber in triplicate counts of individ-ual samples preserved in Lugol’s iodine. DA was ana-lyzed in whole-culture samples (cells plus medium [1]),using HPLC of the FMOC (fluorenylmethoxycarbonyl)derivative [56]. The lower limit of detection was 15 ngmL-1 for the first non-axenic experiment, 3 ng mL-1 forthe second non-axenic experiment, and 7.5 ng mL-1 forthe axenic experiment. RNA was prepared from cellsharvested during an initial time point from the late-exponential (low-toxin-producing) growth phase, anda final time point during the stationary (high-toxin-producing) phase (Figure 1a, 1b; RNA extractionprotocol outlined below).For RT-qPCR evaluation, three non-axenic biological

replicate cultures were sampled daily, from the time ofinoculation until day 10 of growth, for cell counts,whole-culture DA and nutrients. Cell count sampleswere taken by preserving 5 mL of culture with 250 μLof formalin and stored at 4°C until cells were counted(400 cells or the entire slide) on a Sedgwick-Rafterslide. Whole-culture DA samples were taken by freez-ing 15 mL of culture at −20°C; samples were sonicatedat 50% power on ice for 2 min and filtered through a0.2-μm filter prior to analysis. DA samples were ana-lyzed using the Abraxis Domoic Acid ELISA kit ([57],Warminster, PA, USA). The limit of detection was 0.06ng mL-1. Filtered samples were stored at −80°C for nu-trient analyses. Silicate was measured using the molyb-date method [58-60]; phosphate was measured by theascorbic acid-molybdate method [61,62]. Nitrite andnitrate were measured on an auto-analyzer (LachatInstruments, Loveland, CO, USA) using a copper-cadmium reduction and colorimetric assay [61,63,64].Total RNA was extracted daily from each flask begin-ning on day three of growth (Figures 1c, 3, 4; RNA ex-traction protocol outlined below). One RNA samplewas lost on the initial day of extraction, so this resultedin two biological replicates for this time point (Day 3).The remaining RNA samples for the profile through toDay 10 included three biological replicates for eachtime point.

Microarray and cDNA library constructionPs-n strain CL-125 cells from non-axenic cultures wereharvested during the late-exponential through mid-stationary phases, under predominantly toxin-producingconditions. Cultures were split into 250–500 mL ali-quots that were centrifuged for 15 min at 1000 g. Theloose pellets were pooled, and centrifuged again brieflyto remove any residual culture medium. Total RNAwas extracted immediately by homogenizing the cells inTRIzol® (Invitrogen Corporation, Carlsbad, CA, USA).Insoluble material was removed by low-speed centrifu-gation of the samples, which increased quality and yield

of the resulting total RNA. Precipitating twice with saltand ethanol also contributed to high-quality total RNA,as indicated by both 260/280 O.D. ratios and gel elec-trophoresis. Poly (A)+ RNA was then isolated fromtotal RNA using a biotin-labeled oligo(dT)20-streptavi-din kit (Roche Molecular Biochemicals, Indianapolis,IN, USA) following the manufacturer’s instructions.First-strand cDNA was prepared from 5 μg poly (A)+

RNA using Superscript II (Invitrogen, Grand Island, NY,USA), NC-p7 (an RNA chaperone), and oligo pd(TZ)(an oligo-dT primer with some of the internal thymidineresidues replaced with 3-nitropyrrole to minimize mis-priming to internal A-rich sequences). Double-strandedcDNA was generated using RNase H, E. coli DNA poly-merase I, and E. coli ligase. The ends of the cDNAs werepolished with T4 DNA polymerase, and BstXI adaptorswere ligated to the cDNA ends. The cDNAs were thenfractionated on sucrose gradients, ligated into pMD1(a pUC-based vector) and transformed, by electropor-ation, into E. coli DH10B cells [65]. Following an initial li-brary plating, 19,200 individual colonies were picked andstored at −80°C in 15% glycerol for further analysis.

EST sequencing, assembly, and annotation2,220 cDNA inserts were sequenced to verify the qualityof the library and to begin gene discovery. Many of thecDNAs were sequenced more than once in the 5′- and3′- direction, yielding a set of 3,533 Ps-n ESTs. These se-quences are deposited in the NCBI dbEST database[GenBank accession numbers FD476666-FD480212].(Note: Of the sequenced cDNAs, 1,889 were a subset ofthe 5,265 Ps-n cDNAs printed on the microarray (seebelow)). Sequence reactions were run on an automatedDNA sequencer (ABI 3700 with dye terminators); andselected cDNAs were sequenced at ACGT, Inc. (Wheeling,IL, USA). ESTs were edited using Seqman (DNAStar, Inc.,Madison, WI, USA), and ContigExpress (VectorNTI,Carlsbad, CA, USA), as well as manually edited to removelow quality data, poly (A) tails, and vector sequence. ThePs-n ESTs were assembled into consensus or contig se-quences, using a criterion of 95% identity over more than50 nucleotides (Seqman, DNAStar). Average sequencelength of the individual reads was 639 bp (after editing).The ESTs were assembled into 1,550 non-redundant (NR)sequences, indicating a redundancy of ~43% within ourPs-n library. From this, we estimate that approximately3,675 unique genes were printed on the Ps-n micro-array since 5,265 Ps-n cDNAs from the Ps-n librarywere printed on the microarray. The ESTs, final assem-bled NR sequences, and annotations are provided inAdditional files 2, 3, 4 and 5.Assembled sequences were compared against NCBI’s

NR and Swissprot databases using the Basic Local Align-ment Search tools, blastx and tblastn, via theBlast2Go


application [66-68]. The Ps-n sequences from this studywere also compared against the unpublished Pseudo-nitzschia multiseries CLN-47 genome sequence, Assem-bly v1 (October 2011), sequenced by the US Departmentof Energy Joint Genome Institute (JGI) [22]. The refer-ence and target genes specifically discussed in this paperwere further annotated using NCBI’s ORF Finder [69]and BlastP [66,67], and the Center for BiologicalSequence Analysis’ (Technical University of Denmark)ChloroP 1.1, TargetP 1.1, and SignalP 4.1 [70], as well assearching for the conserved diatom AFAP chloroplasttargeting domain [71-73].

Microarray constructioncDNA preparation: 5,265 clones from the Ps-n cDNAlibrary were grown overnight in Luria broth with carbe-nicillin (50 μg mL-1) at 37°C on a shaker table. Three“vector-only” and twelve Homo sapiens cDNA controlclones (J. Pelletier) were also grown under these condi-tions. Ten microliters of bacterial culture were used in100-μL PCR reactions with primers T7 forward (TAATACGACTCACTATAGGG) and M13 reverse (CAGGAAACAGCTATGAC), which flanked the cloning siteof the pMD1 vector. PCR amplification was performedusing HiFI Taq polymerase (Invitrogen Corporation). Aninitial DNA denaturation step at 94°C for 2 min wasfollowed by 35 amplification cycles (0:30 melting at 94°C,0:30 annealing at 55°C, 1:00 extension at 68°C). PCR prod-ucts were purified using MultiScreen size-exclusion filterplates (Millipore, Billerica, MA, USA). The DNA was thenresuspended in 100 μL of nuclease-free de-ionized waterand transferred to clean plates using a mechanical pipet-ting station. DNA quality was verified by 1% agarose gelelectrophoresis for eight samples per 96-well plate; DNAconcentration was determined by PicoGreen fluorescentstaining [63]. Fifty μL of each PCR product was dried byvacuum centrifugation and then resuspended in 10 μL of1.5 M Betain /3X SSC print buffer, yielding an averagefinal concentration of 600 ng μL-1.Ps-n cDNA probes were printed onto CMT-GAPS

slides (Corning, Corning, NY, USA), using a MicroGrid610 TAS array printer (Biorobotics, Woburn, MA, USA)with quill pins. A total of 5,169 Ps-n cDNAs wereprinted in duplicate, and 96 were printed in quadrupli-cate; in addition, 3 “vector-only” cDNAs, 12 H. sapienscontrol cDNAs, and 10 control cDNAs from the SpotRe-port Alien Array Validation System (Stratagene, La Jolla,CA, USA) were printed in duplicate, resulting in a finalchip that included 10,772 features. Spots were printedwith a 32 print-tip head, producing a lay-out representedby 8 × 4 grids. Each grid was sub-divided into two sec-tions, representing replicate spots. Individual featureswere 13 μm in diameter and were separated by 130 μm(from one spot to the next). 0.005 μL of ~600 ng μL-1

DNA (2–3 ng) was transferred to each spot. Final Ps-n ar-rays displayed a strong signal-to-noise ratio, with virtuallyno background, as demonstrated visually (Figure 5). Ex-perimental hybridization results also confirmed the highdegree of reproducibility between replicate spots on thePs-n chip (See Additional files 1 and 2; and, the corre-sponding Gene Expression Omnibus (GEO) [74] file, ac-cession number GSE46845).

RNA preparation and microarray hybridizationsRNA was prepared from cells harvested during both thelate-exponential (low-toxin-producing) and stationary(high-toxin-producing) phases for all three biologicalreplicates. Eight liters of culture were harvested at aninitial time point during the mid- to late-exponentialgrowth phase and the remaining 7 L were harvested at afinal time point during the stationary phase (Figure 1a,1b). Cell suspensions were centrifuged in 0.5-L aliquotsfor 15 min at 1,000 g, which resulted in loose pellets thatwere pooled, split among 2–4, 50-mL conical tubes andspun again briefly to remove any remaining liquid. Tento 20 mL of TRIzol (depending on cell pellet volume)were added to the conical tubes, and the pellets werehomogenized for 60 s, frozen in liquid N, and storedat −80°C until RNA extraction. Total RNA was ex-tracted, as above, cleaned with RNeasy columns (Qiagen,Valencia, CA, USA) and run on formaldehyde agarose gelsto confirm the quality of the RNA.Ten micrograms of Ps-n RNA from each harvest were

spiked with mRNA from the SpotReport Alien ArrayValidation System, incubated for 10 min at 65°C witholigo-dT and then cooled at 25°C for 5 min. Four micro-liters of 1 mM Cy3- or Cy5-conjugated dUTPs wereadded to each RNA sample and the mixtures were incu-bated at 42°C for 2 min. A master mix, including 4.5 μLof 0.2 M DTT, 18 μL of 5X 1st strand buffer, 1.8 μL of25 mM dATP, dGTP and dCTP, 1.8 μL of 10 mM dTTP,and 2 μL of Superscript II reverse transcriptase, was addedto each RNA mixture and incubated for 1 h at 42°C. After1 h, an additional 1 μL of Superscript II was added to eachand the reactions were incubated at 42°C for anotherhour. Starting RNA was degraded by addition of stop so-lution (3 μL of 0.5 M EDTA, pH 8; 3 μL of 1 N NaOH)and incubated for 30 min at 60°C. Labeled cDNA wascleaned using RNeasy columns (Qiagen); Cy3-labeledcDNA and the corresponding Cy5-labeled cDNA thatwere to be compared were combined and loaded onto thesame column. The labeled target cDNA pools were thenhybridized to the probe cDNAs on the Ps-n cDNA micro-arrays (construction described above). Ps-n microarrayswere processed before hybridization by holding themface-down over a steaming water bath for a few seconds,and then snap-drying them on a 95°C heat block. TheDNA was immobilized onto the slides by UV cross-linking

Figure 5 Scanning fluorescence image of the Pseudo-nitzschia multiseries (Ps-n) cDNA microarray hybridized with Cy3- and Cy5-labeledcDNAs from non-toxin-producing vs. toxin-producing cells. 5,169 individual Ps-n cDNAs and 25 control cDNAs were printed in replicate, and96 Ps-n cDNAs were printed in quadruplicate, yielding a final chip including 10,772 features. A representative grid is enlarged to illustrate the sub-division of each 8 × 4 grid into two replicate sections, differentiated by the arrowed lines.


at 65 mJoules. Cross-linked slides were soaked for 15 minin freshly prepared succinic anhydride/sodium borate so-lution with gentle agitation, soaked for 2 min in boilingnuclease-free, de-ionized water and finally, rinsed in 95%ethanol, and spun dry. Processed microarrays were pre-hybridized at room temperature for 1 h. Pre-hybridizationsolution was composed of 50% formamide, 5X SSC, 0.1%SDS, and 1% BSA. Hybridization buffer was composedof 50% formamide, 10X SSC, 0.2% SDS, and 0.26% sal-mon sperm. Labeled cDNA was denatured prior tohybridization by heating for 2 min at 80°C, while thecassette and microarray were pre-warmed at 42°C. ThecDNA was then loaded onto the array, and arrays werehybridized for 16 h at 42°C in humidified chambers.Hybridized arrays were washed successively in 1X SSC,0.03% SDS, 0.1X SSC, 0.01% SDS, and 0.1X SSC, anddried by brief centrifugation.Replicate hybridizations were repeated within each

biological experiment and dye-swapped to account fordifferences in dye labeling and detection efficiencies.Non-axenic experiments 1 and 2 each included six tech-nical replicates, while the axenic experiment includedfour technical replicates.

Microarray image analysis and normalizationDual-channel arrays were scanned at 595 nm (Cy3) and685 nm (Cy5) on ArrayWoRx scanners (Applied Precision,Inc., Issaquah, WA, USA). The scanning system converts

signal from fluors to “pixel” values, which allows thedata to be saved as tiff files. DigitalGenome software(MolecularWare, Cambridge, MA, USA) was then usedto integrate annotated chip information with the tifffiles and to visualize, edit and export the data fornormalization. A loess algorithm was applied to the spotmean intensity values across replicate arrays within eachbiological experiment to correct for systematic biasesusing S+ArrayAnalyzer software (Insightful Corp., Seattle,WA, USA) [75,76]. Quality control included analyzingfinal intensity ratios for the control set of data afternormalization. The normalized intensity data for eachcontrol spike that corresponded to time zero (T0) andtime final (TF) experimental mRNAs in the labeling reac-tions were analyzed using linear regression analysis to ver-ify that the mean integrated intensity across the controlspots was equal (slope ≈ 1). The slope of the linear regres-sion of T0 to TF control intensity values averaged acrossarrays approached 1 for all three biological experiments:Non-axenic experiment 1 = 0.95, R2 = 0.97; Non-axenicexperiment 2 = 0.91, R2 = 0.95; Axenic experiment = 0.91,R2 = 0.98. Any negative values, outliers (defined as twostandard deviations away from the mean for individualspots), and any spots that did not include data for atleast three replicate arrays within each dataset, were re-moved from further analysis. These parameters resultedin the removal of data for <1.5% of the original 10,772features printed on array (final datasets: Axenic experiment


1 = 10,723; Axenic experiment 2 = 10,614; Non-axenicexperiment = 10,630).

Microarray statisticsSignificance analysis of gene expression was performedusing a t-test algorithm modified for multiple tests:Significance Analysis of Microarrays (SAM) [77] [http://www-stat.stanford.edu/~tibs/SAM/]. SAM reports thosegenes with statistically significant differences betweentreatments based on an overall false discovery rate(FDR). A score d(i) is assigned to each gene based onchanges in gene expression relative to the standard devi-ation of repeated measurements. The FDR is an estimateof the percentage of genes identified by chance thatwould have an observed relative difference d(i) greaterthan the expected relative difference dE(i) set by an ad-justable threshold, delta. An FDR of 1% estimates thatfor every 100 genes called significant, less than onewould be identified incorrectly. The FDR may be ad-justed by changing the delta and fold-change thresholds.While SAM does not report individual p-values, eachgene is assigned its own “local FDR” (LFDR), which isthe comparable statistical measurement to identify indi-vidual genes with changes in expression. LFDR can beused to review the data beyond the defined set of differ-entially expressed genes based on FDR. Hong et al. [78]demonstrated an LFDR of 10% as a reliable cut-off tosuccessfully identify changes in expression for specificgenes. While the FDR is considered the most reliablemeasure of the statistically accurate gene list within anexperiment, the LFDR offers a second method forreviewing the statistical likelihood of changes in expres-sion for a particular gene. In our study, we used theoverall FDR to define the initial set of differentiallyexpressed genes. We used the LFDR to confirm theoverall change in gene expression for those transcriptsthat had multiple cDNAs printed on the microarray.Initially, each dataset in our study was analyzed inde-

pendently. Non-axenic experiments 1 and 2 were ana-lyzed for statistical significance using a relatively strin-gent fold-change cut-off of 2.5 to target genes thatwere substantially up- or down-regulated during thetransition to stationary phase, when toxin was pro-duced. A delta value of 0.275 resulted in overall FDRsthat were <1% in both of these experiments. Expressionlevels were consistently lower in the axenic experimentas compared to the non-axenic experiments. For ex-ample, the cDNAs with positive fold-change differencesaveraged 4.07 ± 0.97 in Non-axenic experiment 1, 3.85 ±1.17 Non-axenic experiment 2, and 1.92 ± 0.54 in theAxenic growth experiment. Therefore, a lower fold-change cut-off of 1.5, and a delta value of 0.275, resultedin a comparable FDR that was <2.5% for the Axenic ex-periment. Only those transcripts that were determined to

be significantly up- or down-regulated in all three bio-logical experiments were further analyzed.Two layers of replicates (replicate cDNAs on the array

and replicate hybridizations) were accounted for by firstanalyzing the replicates spots as uncollapsed, independ-ent data points using the normalization and statisticalanalysis described above. Then, replicate spots were av-eraged and collapsed, accordingly, depending on whetheror not they fell into a greater contig. For singletons, bothreplicate spots were required to be statistically signifi-cant in all three biological experiments to be furtherconsidered. In this case, replicate spots were averagedfor a final expression ratio and standard deviation. ForcDNA features that fell into a larger contig, 90% of thecDNAs that fell within the contig were required to besignificantly differentially expressed (based on initialSAM analysis or LFDR) to further consider the overallcontig as up- or down-regulated. In this case, the repli-cates were collapsed by averaging the mean ratios of allcDNAs for a final fold-change ratio and standard devi-ation for the overall contig. The individual cDNAs withincontigs served as additional replicates for these tran-scripts, and the results confirmed the consistent change ingene expression (see Additional file 1). The final fold-change values for those transcripts that were statisticallyhigher or lower in stationary (toxin-producing) as com-pared to late-exponential (low-toxin-producing) growthphase are presented in Tables 3 and 4. Additional datafiles complying with MIAME format [79] were depos-ited at the GEO [74] data repository, accession numberGSE46845.

Primer design and validation for RT-qPCRPrimers were designed manually to be within a length of18–26 nucleotides with a GC content between 50-65%.These values resulted in high sequence specificity andmelting temperatures (Tm) that worked well under ourassay conditions using an annealing temperature of 60°C.JmjC forward, ATPase reverse and SLC6 Ex-Ex forwardhad lower GC contents than the original specifications,but still worked efficiently under these assay conditions.All primer sets were designed for PCR amplicons of 50–200 bp in length. Primers were synthesized by IntegratedDNA Technologies, Inc. (Coralville, IA, USA) and purifiedby standard desalting. Efficiencies of amplification wereinitially determined for each primer set by running stand-ard curves with 5-fold serial dilutions of Ps-n cDNA de-rived from stationary phase cultures, as well as genomicDNA. PCR conditions are described, below. Primer se-quences and information can be found in Tables 2 and 5.Reported efficiencies in the tables correspond with the ini-tial cDNA standard curve analyses. Standard curves werealso run using 2-fold serial dilutions of pooled cDNA fromthe experimental samples combined in equal amounts.

http://www-stat.stanford.edu/~tibs/SAM/

http://www-stat.stanford.edu/~tibs/SAM/


The primer sets used in the Si-limitation experimentalanalyses showed efficiencies >95%, with R2 values >0.99.Primers that span an exon-exon junction were designedfor one reference and two of the target genes; these primersets did not yield a product using gDNA as a template.

RNA isolation, cDNA synthesis and RT-qPCRsTotal RNA was extracted daily from each flask begin-ning on day three of growth. Cells were collected from250 mL of culture by filtering onto a 5.0-μm, 47-mmmembrane filter (MF-Millipore mixed cellulose ester).Filters were transferred to 50 mL conical tubes and 3 mLof TRIzol were added. Cells were washed quickly andgently from the filter and homogenized at full speed witha Polytron homogenizer (Kinematica, Inc, Bohemia, NY)for 90 s. Samples were incubated at room temperaturefor 5 min following homogenization and centrifuged at3000 g for 10 min at 4°C in order to pellet cellular debris.200 μL of chloroform was added for every 1 mL ofhomogenate and samples were incubated for 3 minwith periodic shaking at room temperature. Sampleswere centrifuged at 12,000 g for 20 min at 4°C in orderto separate the aqueous and organic phases. 75-80% ofthe aqueous phase was transferred to fresh tubes andan equal volume of 70% EtOH was added. The RNA-EtOH mixture was cleaned using RNeasy mini columnswith on-column RNAase-free DNAase digestion (Qiagen).Clean RNA samples were eluted in 100 μL of DEPC-treated water, and stored at −80°C until analyzed. RNAconcentrations were analyzed using a Nanodrop 2000spectrophotometer (Wilmington, DE, USA), and RNAquality was verified by gel electrophoresis using Lonza1.2% RNA cassettes (Walkersville, MD, USA). RNA sam-ples were diluted to 20 ng μL-1. 600 ng of total RNA fromeach sample was reverse transcribed using the iscriptcDNA synthesis kit in 50 μL volume reactions, using bothpoly A and random hexamer primers (Bio-Rad Laborator-ies, Inc., Hercules, CA, USA). The reverse transcriptionreaction was carried out by incubating at 25°C for 5 min,followed by 42°C for 30 min. The enzyme was deactivatedby heating to 85°C for 5 min and samples were held at 4°Cuntil retrieved and stored at −20°C.RT-qPCR reactions were set up as follows: 10 μL of

SYBR Green PCR mix (Bio-Rad Laboratories, Inc.),0.75 μL of cDNA, 0.2 μM of forward primer, 0.2 μM ofreverse primer, and nuclease-free water to a final vol-ume of 20 μL. An exception was that the β-tubulin Ex-Exefficiency was within the 95-105% range using 0.4 μM forboth forward and reverse primers. Each experimentalcDNA was amplified in triplicate for each primer set usingthe following cycling parameters: 1) 95°C for 3 min; 2) 95°Cfor 10 s; 3) 60°C for 15 s; 4) 72°C for 30 s (plate read);5) repeat 39 more cycles of steps 2–4; 6) 72°C for 10 min;8) melting curve analysis from 65-95°C in 0.5°C increments

every 5 s; and 9) hold at 4°C. Cq values were deter-mined for each reaction at 150 relative fluorescentunits.

Evaluation of DNA contamination in RT-qPCRsqPCR reactions were run on 1 μL of each RNA from theSi-limitation growth experiment to test for amplificationdue to contaminating DNA. The absence of DNA con-tamination was further confirmed by using both stand-ard and exon-exon spanning primer sets in parallel forone control and two target genes in the RT-qPCR reac-tions (Figures 3 and 4).

Analysis of candidate reference gene expression stabilityCq values were inputted into the GeNorm plus algo-rithm [20,80] and a stability value (M-value) was calcu-lated for each gene (Figure 2A, 2B). Genes with thelowest M-values are considered the most stable; M-values <0.5 are ideal for use in normalization of qPCRdata. The optimum number of reference genes to use fornormalization is determined by calculating the geometricaverage of the two, three, four, and five most stable genes.The pairwise-variation between subsequent normalizationfactors is calculated and when the variation is below 0.15,the amount of change caused by the addition of thenew control gene is considered negligible, and thereforeunnecessary to include in subsequent normalizationcalculations.

Normalization, quantification, and statistics of RT-qPCRanalysisThe arithmetic mean of triplicate technical replicateswas calculated and used for subsequent calculations.The ΔCq values were calculated by the difference be-tween each sample and the average Cq of the chosen ref-erence point, which was the first time point (T3).Relative quantities (RQ) were calculated by exponenti-ation of the ΔCqs (2ΔCq). RQ values of the target genes,for each sample, were divided by the geometric averageof the chosen reference genes’ RQ values, resulting in anormalized relative quantity (NRQ) [19]. The arithmeticmean and standard deviation of biological replicate NRQswere then calculated and plotted (Figures 3 and 4). NRQvalues were log transformed (Log2 NRQ) into Cq’ valuesfor statistical analysis [81]. Statistically significant changesin gene expression were determined using a general linearmodel analysis of variance (ANOVA) with Bonferronipost-hoc test. Statistical analyses were performed with 95%confidence intervals (p < 0.05) using Minitab16 statisticalsoftware (State College, PA, USA). Additional files 6 and 7contain the complete set of RT-qPCR data and statisticalresults. RT-qPCR data analyses and reporting were in ac-cordance with MIQE guidelines [82,83].


Availability of supporting dataThe microarray data supporting the results discussedin this paper are included within the paper, and inAdditional files 1 and 2. The microarray data are alsoavailable in the NCBI’s GEO repository, and are ac-cessible through the GEO Series accession numberGSE46845. The sequence data and annotations arepresented in Additional files 2, 3, 4 and 5. The ESTsare also available through the NCBI dbEST database[GenBank accession numbers FD476666-FD480212].The RT-qPCR data and statistical results are availablein Additional files 6 and 7.

Additional files

Additional file 1: Fold-change data and statistics for cDNAreplicates on the Ps-n microarray for each of the transcriptsdiscussed in this paper.

Additional file 2: Excel file with the annotation and array data forthe entire set of cDNA clones printed on the Ps-n microarray.

Additional file 3: Fasta file with all of the Ps-n ESTs from this study.

Additional file 4: Fasta file with all of the assembled sequencesfrom this study.

Additional file 5: Contig alignments in .ace format. A number of thealignments (46A5, 53B6, 73D12, 135H6, 165G9, 177F1, PSN0011, PSN0014,PSN0016, PSN0019, PSN0032, PSN0042, PSN0060, PSN0072, PSN0080,PSN0100, PSN0332, PSN0547, PSN0918, and PSN1327) include sequencesfrom the JGI Pseudo-nitzschia genome project [22] for comparison; thesesequences are designated within the contig by the JGI modeled genename or genome location. Note that contigs corresponding with threetranscripts discussed in the manuscript, PSN0014, PSN0016, and PSN0052,showed splice variants. Expression of the individual cDNAs within thesecontigs were not significantly different in the microarray analysis (seeAdditional file 1).

Additional file 6: RT-qPCR data.

Additional file 7: RT-qPCR statistical results.

AbbreviationsASP: Amnesic shellfish poisoning; cDNA: Complementary DNA; DA: Domoicacid; EF-1α: Elongation factor 1-alpha, eIF-2, Elongation initiation factor 2;ESTs: Expressed sequence tags; FCP: Fucoxanthin-chlorophyll a-c bindingprotein; FDR: False discovery rate; FMOC: Fluorenylmethoxycarbonyl;GABA: γ-aminobutyric; GAPDH: Glyceraldehyde-3-phosphate dehydrogenase;GDH: Glutamate dehydrogenase; HPLC: High performance liquidchromatography; LFDR: Local false discovery rate; MIAME: Minimuminformation about a microarray experiment; PCR: Polymerase chain reaction;PEPCK: Phosphoenolpyruvate carboxykinase; PGK: Phosphoglycerate kinase;PFK: Phosphofructokinase; Ps-n: Pseudo-nitzschia multiseries; RT-qPCR: Reverse-transcription quantitative PCR; SAM: Significance analysis of microarrays;SLC6: Solute carrier family 6.

Competing interestsThe authors declare that they have no competing interests.

Authors’ contributionsKRB contributed to the design, completion, and analysis of the RT-qPCR andmicroarray studies, and wrote the manuscript. BMH contributed to thedesign, completion, and analysis of the RT-qPCR experiments. KRB and JPconstructed the cDNA library and sequencing was completed in JP’s lab atMcGill University (or ACGT, Inc., for a small subset of samples). KRB and SMcompleted the microarray construction and data analysis. DLR participated indata review and in revising the manuscript. SSB participated in the design ofthe array studies, data review, and in drafting the manuscript. HPLC assayswere completed in SSB’s lab in Moncton, NB (Fisheries and Oceans Canada).

DAH contributed to the design of the RT-qPCR studies, ELISA domoic acidanalyses, data review and in revising the manuscript. DEH contributed to thedesign of the RT-qPCR and microarray studies, data review and analyses, anddrafting the manuscript. Microarray studies were completed in DEH’s lab atMIT; RT-qPCR studies were completed in KRB’s lab at PSU. All authors readand approved the final manuscript.

AcknowledgementsWe thank Claude Léger for running HPLC domoic acid analyses for themicroarray studies; we also thank Michael Quilliam for running HPLC andmass spectrometry to confirm validity of the ELISA DA analysis in theRT-qPCR studies. We thank Nhi Nguyen and Isabelle Harvey for assistance inlibrary construction and sequencing, and Xuan Shirley Li for assistance duringmicroarray construction. We thank Charlie Whittaker and Sebastian Hoerschfor assistance with bioinformatics analysis. We thank Vittoria Roncalli for pro-viding an introduction to the Blast2Go application. We thank Petra Lenz andBrad Jones for generously providing space and computer support for KRB towork on data analysis during Jan 2012. We thank Christopher Wilk and MeganCooper for completing cell counts in the strain GGB1 growth experiment. Wethank Katherine Lozano and Lauren Oakes for running ELISA assays. We thankNichole DiLuzio for technical lab assistance. We thank Micaela Parker (Universityof Washington) and the JGI Pseudo-nitzschia genome team for access to thegenome data before public release, and permission to include JGI genomeproject sequences in our contig alignments for comparison. We also thankMichael Carlson and Kyle Frishkorn (University of Washington) for providing thePs-n GGB1 culture. We thank Thomas Boucher for consultation on statisticalanalysis of RT-qPCR data, including a tutorial of the Minitab 16 software. Wethank Brian Howes, Sara Sampieri Horvet, and Jennifer Benson at the CoastalSystems Program, University of Massachusetts, Dartmouth, for running thesilicate, nitrite/nitrate, and phosphate analyses. We thank Don Anderson, MarkHahn, and Senjie Lin for their advice on the microarray studies. We thankJefferson Turner and Junne Kamihara for helpful discussions throughout thecourse of this work and in preparation of the manuscript. And, we thank RonTaylor, Steve Fiering, Chuck Wise, and George Tuthill for all of their support andencouragement of this project. Financial support for this research was providedby the Woods Hole Oceanographic Institution Academic Programs Office, thePlymouth State University Graduate Programs Office, and the New HampshireIDeA Network of Biological Research Excellence (NH-INBRE), with grants fromthe National Center for Research Resources (5P20RR030360-03) and the NationalInstitute of General Medical Sciences (8P20GM103506-03), National Institutes ofHealth.

Author details1Department of Biological Sciences, Plymouth State University, MSC 64, 17High St., Plymouth, NH 03264, USA. 2Koch Institute, Massachusetts Institute ofTechnology, 76-553, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.3Present address: Mascoma Corporation, 67 Etna Road Suite 300, Lebanon,NH 03766, USA. 4Fisheries and Oceans Canada, Gulf Fisheries Centre, P.O. Box5030, Moncton, New Brunswick E1C 9B6, Canada. 5Biology Department, ClarkUniversity, 950 Main Street, Worcester, MA 01610, USA. 6Present address:Vertex Pharmaceuticals, 130 Waverly Street, Cambridge, MA 02139, USA.7Department of Biochemistry, McGill University, 3655 Promenade Sir WilliamOsler, Montreal, Quebec H3G 1Y6, Canada. 8Department of Microbiology andImmunology, Vail Building Room 208, Dartmouth Medical School, Hanover,NH 03755, USA.

Received: 12 June 2013 Accepted: 18 October 2013Published: 1 November 2013

References1. Bates SS, Bird CJ, de Freitas ASW, Foxall R, Gilgan M, Hanic LA, Johnson GR,

McCulloch AW, Odense P, Pocklington R, Quilliam MA, Sim PG, Smith JC,Subba Rao DV, Todd ECD, Walter JA, Wright JLC: Pennate diatom Nitzschiapungens as the primary source of domoic acid, a toxin in shellfish fromeastern Prince Edward Island, Canada. Can J Fish Aquat Sci 1989,46:1203–1215.

2. Douglas DJ, Bates SS: Production of domoic acid, a neurotoxic aminoacid, by an axenic culture of the marine diatom Nitzschia pungens f.multiseries Hasle. Can J Fish Aquat Sci 1992, 49:85–90.

3. Douglas DJ, Ramsey UP, Walter JA, Wright JLC: Biosynthesis of theneurotoxin domoic acid by the marine diatom Nitzschia pungens forma

http://www.biomedcentral.com/content/supplementary/1471-2199-14-25-S1.pdf

http://www.biomedcentral.com/content/supplementary/1471-2199-14-25-S2.xlsx

http://www.biomedcentral.com/content/supplementary/1471-2199-14-25-S3.fas

http://www.biomedcentral.com/content/supplementary/1471-2199-14-25-S4.fas

http://www.biomedcentral.com/content/supplementary/1471-2199-14-25-S5.ace

http://www.biomedcentral.com/content/supplementary/1471-2199-14-25-S6.xlsx

http://www.biomedcentral.com/content/supplementary/1471-2199-14-25-S7.docx


multiseries, determined with [13C]-labelled precursors and nuclearmagnetic resonance. J Chem Soc Chem Commun 1992, 1992:714–7156.

4. Wright JLC, Boyd RK, de Freitas ASW, Falk M, Foxall RA, Jamieson WD,Laycock MV, McCulloch AW, McInnes AG, Odense P, Pathak VP, Quilliam MA,Ragan MA, Sim PG, Thibault P, Walter JA, Gilgan M, Richard DJA, Dewar D:Identification of domoic acid, a neuroexcitatory amino acid, in toxicmussels from eastern Prince Edward Island. Can J Chem 1989, 67:714–716.

5. Takemoto T, Daigo K: Constituents of Chondria armata. Chem Pharm Bull1958, 6:578–580.

6. Ramsdell JS: The molecular and integrative basis to domoic acid toxicity.In Phycotoxins: Chemistry and Biochemistry. Edited by Botana L. Cambridge,MA: Blackwell Publishing Professional; 2007:223–250.

7. Lelong A, Hégaret H, Soudant P, Bates SS: Pseudo-nitzschia(Bacillariophyceae) species, domoic acid and amnesic shellfishpoisoning: revisiting previous paradigms. Phycologia 2012, 51:168–216.

8. Trainer VL, Bates SS, Lundholm N, Thessen AE, Cochlan WP, Adams NG, TrickCG: Pseudo-nitzschia physiological ecology, phylogeny, toxicity,monitoring and impacts on ecosystem health. Harmful Algae 2012,14:271–300.

9. Bates SS: Ecophysiology and metabolism of ASP toxin production. InPhysiological Ecology of Harmful Algal Blooms. Edited by Anderson DM,Cembella AD, Hallegraeff GM. Heidelberg: Springer-Verlag; 1998:405–426.

10. Bates SS, Garrison DL, Horner RA: Bloom dynamics and physiology ofdomoic-acid-producing Pseudo-nitzschia species. In Physiological Ecologyof Harmful Algal Blooms. Edited by Anderson DM, Cembella AD, HallegraeffGM, Anderson DM, Cembella AD, Hallegraeff GM. Heidelberg: Springer-Verlag; 1998:267–292.

11. Bates SS, Trainer VL: The ecology of harmful diatoms. In Ecology of HarmfulAlgae Ecological Studies. Volume 189. Edited by Granéli E, Turner J.Heidleberg: Springer-Verlag; 2006:81–93.

12. Ramsey UP, Douglas DJ, Walter JA, Wright JLC: Biosynthesis of domoic acidby the diatom Pseudo-nitzschia multiseries. Nat Toxins 1998, 6:137–146.

13. Savage TJ, Smith GJ, Clark AT, Saucedo PN: Condensation of theisoprenoid and amino precursors in the biosynthesis of domoic acid.Toxicon 2012, 59:25–33.

14. Pan Y, Bates SS, Cembella AD: Environmental stress and domoic acidproduction by Pseudo-nitzschia: a physiological perspective. Nat Toxins1998, 6:127–135.

15. Bates SS, Douglas DJ, Doucette GJ, Léger C: Enhancement of domoic acidproduction by reintroducing bacteria to axenic cultures of the diatomPseudo-nitzschia multiseries. Nat Toxins 1995, 3:428–435.

16. Osada M, Stewart JE: Gluconic acid/gluconolactone: physiologicalinfluences on domoic acid production by bacteria associated withPseudo-nitzschia multiseries. Aquat Microb Ecol 1997, 12:203–209.

17. Stewart JE: Bacterial involvement in determining domoic acid levelsapparent in Pseudo-nitzschia multiseries cultures. Aquat Microb Ecol 2008,50:135–144.

18. Kotaki Y, Koike K, Yoshida M, Thuoc CV, Huyen NTM, Hoi NC, Fukuyo Y,Kodama M: Domoic acid production in Nitzschia sp. isolated from ashrimp-culture pond in Do Son, Vietnam. J Phycol 2000, 36:1057–1060.

19. Hellemans J, Mortier G, De Paepe A, Speleman F, Vandesompele J: qBaserelative quantification framework and software for management andautomated analysis of real-time quantitative PCR data. Genome Biol 2007,8:R19.

20. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A,Speleman F: Accurate normalization of real-time quantitative RT-PCR databy geometric averaging of multiple internal control genes. Genome Biol2002, 3:1–12.

21. Gunjan A, Paik J, Verreault A: Regulation of histone synthesis andnucleosome assembly. Biochimie 2005, 87:625–635.

22. US Department of Energy Joint Genome Institute, Armbrust EV, Parker MS,Rocap G, Jenkins B, Bates SS: Pseudo-nitzschia multiseries CLN-47 genomesequence, Assembly v1. 2011. [http://genome.jgi.doe.gov/Psemu1/Psemu1.home.html]

23. Cui B, Liu Y, Gorovsky M: Deposition and function of histone H3 variantsin Tetrahymena thermophila. Mol Cell Biol 2006, 26:7719–7730.

24. Elsaesser S, Goldberg A, Allis C: New functions for an old variant: nosubstitute for histone H3.3. Curr Opin Genet Dev 2010, 20:110–117.

25. Jin J, Cai Y, Li B, Conaway R, Workman J, Conaway J, Kusch T: In and out:histone variant exchange in chromatin. Trends Biochem Sci 2005,30:680–687.

26. Yu L, Gorovsky M: Constitutive expression, not a particular primarysequence, is the important feature of the H3 replacement variant hv2 inTetrahymena thermophila. Mol Cell Biol 1997, 17:6303–6310.

27. Anju V, Kapros T, Waterborg J: Identification of a replication-independentreplacement histone H3 in the basidiomycete Ustilago maydis. J BiolChem 2011, 286:25790–25800.

28. Siaut M, Heijde M, Mangogna M, Montsant A, Coesel S, Allen A,Manfredonia A, Falciatore A, Bowler C: Molecular toolbox for studyingdiatom biology in Phaeodactylum tricornutum. Gene 2007, 406:23–35.

29. Pan Y, Subba Rao DV, Mann KH, Brown RG, Pocklington R: Effects of silicatelimitation on production of domoic acid, a neurotoxin, by the diatomPseudo-nitzschia multiseries (Hasle). I. Batch culture studies. Mar Ecol ProgSer 1996, 131:225–233.

30. Valenzuela J, Mazurie A, Carlson RP, Gerlach R, Cooksey KE, Peyton BM, FieldsMW: Potential role of multiple carbon fixation pathways during lipidaccumulation in Phaeodactylum tricornutum. Biotechnol Biofuels 2012, 5:40.

31. Corstjens PLAM, González EL: Effects of nitrogen and phosphorus availabilityon the expression of the coccolith-vesicle V-ATPase (subunit c) ofPleurochrysis (Haptophyta). J Phycol 2004, 40:82–87.

32. Dyhrman ST, Haley ST, Birkeland SR, Wurch LL, Cipriano MJ, McArthur AG:Long serial analysis of gene expression for gene discovery andtranscriptome profiling in the widespread marine coccolithophoreEmiliania huxleyi. Appl Environ Microbiol 2006, 72:252–260.

33. Reinbothe S, Reinbothe C: The regulation of enzymes involved inchlorophyll biosynthesis. Eur J Biochem 1996, 237:323–343.

34. Kajander T, Merckel M, Thompson A, Deacon A, Mazur P, Kozarich J,Goldman A: The structure of Neurospora crassa 3-carboxy-cis, cis-muconate lactonizing enzyme, a beta propeller cycloisomerase. Structure2002, 10:483–492.

35. Mazur P, Henzel W, Mattoo S, Kozarich J: 3-Carboxy-cis, cis-muconate lac-tonizing enzyme from Neurospora crassa: an alternate cycloisomerasemotif. J Bacteriol 1994, 176:18–28.

36. Mazur P, Pieken W, Budihas S, Williams S, Wong S, Kozarich J: Cis, cis-muconatelactonizing enzyme from Trichosporon cutaneum: evidence for a novel classof cycloisomerases in eucaryotes. Biochemistry 1994, 33:1961–1970.

37. Thomason L, Court D, Datta A, Khanna R, Rosner J: Identification of theEscherichia coli K-12 ybhE gene as pgl, encoding 6-phosphogluconolactonase. J Bacteriol 2004, 186:8248–8253.

38. Zimenkov D, Gulevich A, Skorokhodova A, Biriukova I, Kozlov Y, Mashko S:Escherichia coli ORF ybhE is pgl gene encoding 6-phosphogluconolactonase(EC 3.1.1.31) that has no homology with known 6PGLs from other organ-isms. FEMS Microbiol Lett 2005, 224:275–280.

39. Henningsen B: Bioinformatic and Gene Expression Analysis of an SLC6Homolog in the Toxin-Producing Marine Diatom Pseudo-nitzschia multiseries.MS thesis. Plymouth State University, Biological Sciences; 2012.

40. Beuve N, Rispail N, Laine P, Cliquet J-B, Ourry A, Le Deunff E: Putative role ofgamma-aminobutyric acid (GABA) as a long-distance signal in up-regulationof nitrate uptake in Brassica napus L. Plant Cell Environ 2004, 27:1035–1046.

41. Chevrot R, Rosen R, Haudecoeur E, Cirou A, Shelp B, Ron E, Faure D: GABAcontrols the level of quorum-sensing signal in Agrobacteriumtumefaciens. Proc Natl Acad Sci USA 2006, 103:7460–7464.

42. Palanivelu R, Brass L, Edlund AF, Preuss D: Pollen tube growth andguidance is regulated by POP2, an Arabidopsis gene that controls GABAlevels. Cell 2003, 114:47–59.

43. Johnston M, Gallacher S, Smith EA, Glover LA: Detection of N-acyl homo-serine lactones in marine bacteria associated with production and bio-transformation of sodium channel blocking toxins and the microflora oftoxin-producing phytoplankton. In Harmful Algal Blooms. Edited byHallegraeff GM, Blackburn SI, Bolch CJ, Lewis RJ. Paris: IntergovernmentalOceanographic Commission of UNESCO; 2001:375–378.

44. Amin S, Parker M, Armbrust E: Interactions between diatoms and bacteria.Microbiol Mol Biol R 2012, 76:667–684.

45. Maldonado MT, Hughes MP, Rue EL, Wells ML: The effect of Fe and Cu ongrowth and domoic acid production by Pseudo-nitzschia multiseries andPseudo-nitzschia australis. Limnol Oceanogr 2002, 47:515–526.

46. Rue E, Bruland K: Domoic acid binds iron and copper: a possible role forthe toxin produced by the marine diatom Pseudo-nitzschia. Mar Chem2001, 76:127–134.

47. Wells ML, Trick CG, Cochlan WP, Hughes MP, Trainer VL: Domoic acid: thesynergy of iron, copper, and the toxicity of diatoms. Limnol Oceanogr2005, 50:1908–1917.

http://genome.jgi.doe.gov/Psemu1/Psemu1.home.html

http://genome.jgi.doe.gov/Psemu1/Psemu1.home.html


48. Carnal N, Black C: Phosphofructokinase activities in photosyntheticorganisms: the occurrence of pyrophosphate-dependent 6-phosphofructokinase in plants and algae. Plant Physiol 1983, 71:150–155.

49. Lea PJ, Chen ZH, Leegood RC, Walker RP: Does phosphoenolpyruvatecarboxykinase have a role in both amino acid and carbohydratemetabolism? Amino Acids 2001, 20:225–241.

50. Lehmann T, Ratajczak L: The pivotal role of glutamate dehydrogenase(GDH) in the mobilization of N and C from storage material toasparagine in germinating seeds of yellow lupine. J Plant Physiol 2008,165:149–158.

51. Qui X, Wie W, Lian X, Zhang Q: Molecular analyses of the rice glutamatedehydronase gene family and their response to nitrogen andphosphorous deprivation. Plant Cell Rep 2009, 28:1115–1126.

52. Parrish CC, de Freitas ASW, Bodennec G, MacPherson EJ, Ackman RG: Lipidcomposition of the toxic marine diatom, Nitzschia pungens.Phytochemistry 1991, 30:113–116.

53. Black P, DiRusso C: Yeast acyl-CoA synthetases at the crossroads of fattyacid metabolism and regulation. Biochim Biophys Acta 2007, 1771:286–298.

54. Shockey J, Browse J: Genome-level and biochemical diversity of the acyl-activating enzyme superfamily in plants. Plant J 2011, 66:143–160.

55. Guillard RRL, Ryther JH: Studies of marine planktonic diatoms. I. Cyclotellanana Hustedt, and Detonula confervacea (Cleve) Gran. Can J Microbiol1962, 8:229–239.

56. Pocklington R, Milley JE, Bates SS, Bird CJ, de Freitas ASW, Quilliam MA: Tracedetermination of domoic acid in seawater and phytoplankton by high-performance liquid chromatography of the fluorenylmethoxycarbonyl(FMOC) derivative. Internat J Environ Anal Chem 1990, 38:351–368.

57. Garthwaite I, Ross K, Miles C, Hansen R, Foster D, Wilkins A, Towers N:Polyclonal antibodies to domoic acid, and their use in immunoassays fordomoic acid in sea water and shellfish. Nat Toxins 1998, 6:93–104.

58. Clesceri LS, Greenberg AE, Trussell RR (Eds): Standard Methods for theExamination of Water and Wastewater. 17th edition. Washington, D.C:American Public Health Association; 1989.

59. Mullin JB, Riley JP: The colorimetric determination of silicate with specialreference to sea and natural waters. Anal Chim Acta 1955, 12:162.

60. Strickland JDH, Parsons TR: A Manual of Seawater Analysis. In FisheriesResearch Board of Canada. ; 1965.

61. Eaton AD, Clesceri LS, Greenberg AE (Ed): Standard Methods for theExamination of Water and Wastewater. 19th edition. Washington, D.C:American Public Health Association; 1995.

62. Murphy J, Riley JP: A modified single solution method for determinationof phosphate in natural waters. Anal Chim Acta 1962, 27:31–36.

63. Bendschneider K, Robinson R: A new spectrophotometic method for thedetermination of nitrite in seawater. J Mar Res 1952, 11:87–96.

64. Wood E, Armstrong F, Richards F: Determination of nitrate in sea water bycadmium copper reduction to nitrite. J Mar Biol Assoc UK 1967, 47:23–31.

65. Das M, Harvey H, Chu LL, Sinha M, Pelletier J: Full-length cDNAs: morethan just reaching the ends. Physiol Genomics 2001, 6:57–80.

66. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ:Gapped BLAST and PSI-BLAST: a new generation of protein databasesearch programs. Nucleic Acids Res 1997, 25:3389–3402.

67. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignmentsearch tool. J Mol Biol 1990, 215:403–410.

68. Conesa A, Götz S, García-Gómez J, Terol J, Talón M, Robles M: Blast2GO: auniversal tool for annotation, visualization and analysis in functionalgenomics research. Bioinformatics (Oxford, England) 2005, 21:3674–3676.

69. The Open Reading Frame Finder. http://www.ncbi.nlm.nih.gov/gorf/gorf.html.70. Emanuelsson O, Brunak S, Von Heijne G, Nielsen H: Locating proteins in the

cell using TargetP, SignalP and related tools. Nat Protoc 2007, 2:953–971.71. Apt KE, Zaslavkaia L, Lippmeier JC, Lang LC, Kilian O, Wetherbee R,

Grossman AR, Kroth PG: In vivo characterization of diatom multipartiteplastid targeting signals. J Cell Sci 2002, 115:4061–4069.

72. Gruber A, Vugrinec S, Hempel F, Gould SB, Maier U-G, Kroth PG: Protein tar-geting into complex diatom plastids: functional characterisation of aspecific targeting motif. Plant Mol Biol 2007, 64:519–530.

73. Kilian O, Kroth PG: Identification and characterization of a new conservedmotif within the presequence of proteins targeted into complex diatomplastids. Plant J 2005, 41:175–183.

74. Edgar R, Barrett T: NCBI GEO standards and services for microarray data.Nat Biotechnol 2006, 24:1471–1472.

75. Park T, Yi SG, Kang SH, Lee S, Lee YS, Simon R: Evaluation of normalizationmethods for microarray data. BMC Bioinformatics 2003, 4:33.

76. Quackenbush J: Microarray data normalization and transformation.Nat Genet 2002, 32(Suppl):496–501.

77. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarraysapplied to the ionizing radiation response. Proc Natl Acad Sci USA 2001,98:5116–5121.

78. Hong W, Tibshirani R, Chu G: Local false discovery rate facilitatescomparison of different microarray experiments. Nucleic Acids Res 2009,37:7483–7497.

79. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C,Aach J, Ansorge W, CA B, Causton H, et al: Minimum information about amicroarray experiment (MIAME)-toward standards for microarray data.Nat Genet 2001, 4:365–371.

80. Vandesompele J, Kubista M, Pfalffl MW: Reference gene validationsoftware for improved normalization. In Real-time PCR: Current Technologyand Application. Edited by Logan J, Edwards K, Saunders N. Norfolk: CaisterAcademic Press; 2009:47–64.

81. Rieu I, Powers SJ: Real-time quantitative RT-PCR: design, calculations, andstatistics. Plant Cell 2009, 21:1031–1033.

82. Bustin S, Beaulieu J-F, Huggett J, Jaggi R, Kibenge F, Olsvik P, Penning L,Toegel S: MIQE precis: Practical implementation of minimum standardguidelines for fluorescence-based quantitative real-time PCR experi-ments. BMC Molec Biol 2010, 11:74.

83. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R,Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT: The MIQEguidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 2009, 55:611–622.

doi:10.1186/1471-2199-14-25Cite this article as: Boissonneault et al.: Gene expression studies for theanalysis of domoic acid production in the marine diatom Pseudo-nitzschia multiseries. BMC Molecular Biology 2013 14:25.

Submit your next manuscript to BioMed Centraland take full advantage of:

• Convenient online submission

• Thorough peer review

• No space constraints or color figure charges

• Immediate publication on acceptance

• Inclusion in PubMed, CAS, Scopus and Google Scholar

• Research which is freely available for redistribution

Submit your manuscript at www.biomedcentral.com/submit

http://www.ncbi.nlm.nih.gov/gorf/gorf.html

Documents

Gene expression studies for the analysis of domoic acid production in the marine diatomPseudo